{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Matplotlib图鉴——基础箱线图\n",
    "\n",
    "## 公众号：可视化图鉴"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3.3\n",
      "1.2.0\n",
      "1.19.4\n"
     ]
    }
   ],
   "source": [
    "import matplotlib\n",
    "print(matplotlib.__version__) #查看Matplotlib版本\n",
    "import pandas as pd\n",
    "print(pd.__version__) #查看pandas版本\n",
    "import numpy as np\n",
    "print(np.__version__) #查看numpy版本\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rcParams['font.sans-serif'] = ['STHeiti'] #设置中文"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "注意，代码在以下环境全部通过测试:\n",
    "- Python 3.7.1\n",
    "- Matplotlib == 3.3.3\n",
    "- pandas == 1.2.0\n",
    "- numpy == 1.19.4\n",
    "\n",
    "因版本不同，可能会有部分语法差异，如有报错，请先检查拼写及版本是否一致！"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础箱线图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLhhRJktQlQ4okSeqSIUWSJHXJkCJJkrpkSJEkSV0ypEiSpC4ZUiRJUpcMKZIkqUuGFEmS1CVDiiRJ6pIhRZIkdcmQIkmSumRIkSRJXTKkSJKkLhlSJElSlwwpkiSpS4YUSZLUJUOKJEnqkiFFkiR1yZAiSZK61EVISXJmkh9NfT1o7JokSdK4loxdQJLdgZtW1cqxa5EkSf3ooSXlzsA3xy5CkiT1pYeQcnfgnkm+n+Qrs3X1JDk2yeokqy+88MIRSpQkSQuth5Dyf4A7VdXBwLOAU5LcbvqAqjq1qlZV1aoDDjhglCIlSdLCGj2kVNU5VXXR8P2ZwKnAb41blSRJGtuoISXJoUkOn7F5L+C6MeqRJEn9GLslZV/gn5PcCiDJvYFjgNNHrUqSJI1u1CnIVfXvSV4N/NswFfnHwOOq6idj1iVJksY3+jopVfV24O0jlyFJkjozdnePJEnSrAwpkiSpS4YUSZLUJUOKJEnqkiFFkiR1yZAiSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLoy+LvyPbb7/9WLNmzYJeM8mCXWv58uVcfPHFC3a9HYWfuyQtDEPKNlizZg1VNXYZ281C/mHckfi5S9LCsLtHkiR1yZAiSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLhhRJktQlQ4okSeqSIUWSJHXJkCJJkrpkSJEkSV0ypEiSpC4ZUiRJUpcMKZIkqUuGFEmS1CVDiiRJ6pIhRZIkdWnJlhyc5MHAE4FrgLVzeMmuwO7Auqp6+paXJ0mSFqstCinAzYCnAlfM2L6MDYPLElpAuRK4cGsLlCRJi9OWdvdcDRRw66rap6r2AW4JBHjCZNuw/Y8Ahue3ndeqJUnSTm+TISXJgUmmW1smLSXrprbVjMeZ2yVJkrbY5rp7fh84Ickvhud7Do/fTDIJKhke35LkyqnX7jVPNUqSpEVocyHlIuBzwFXAtcCtgJsDXxqeT85xG+DbwPlTr70tcOQ81ipJkhaRTYaUqnoH8I7J8yS/DTwQeGZV/XLYthetxeWNVXX61LG/jyFFkiRtpS2d3bPr8Hj/qa6dpcPj4Ukunzr2MIAkDwRSVZ/d+jIlSdJis6UhZS/aGJR/Zf1YlImThsease8zw7ZdkSRJmqM5h5QkvwGsBB4HXE4bpwJwEG1q8k+H56uAHwM/B3ajtbTsNz/lSpKkxWJLWlKeDNy7qu44vTHJycAPq+ro4fkbgG9U1ZPnq0hJkrT4bDakJNm1qtbSVpm9atj2eFpLyTraWiu/kuR+tG6eC4B7DUvo7zV8LQP+qaqumuUSkiRJG9hkSElyBPCGobVkHXD9sOtxwGOmDi3g8zOef5L1Y1MK+Bjru4gkSZI2aXMtKXcBHgD8Bi1o/HeSBwCvGL6uAU6grZ/yR8Nr7gOcAjwbOBvYe/i6eD4LlyRJO7fNhZSPAg8HDgeOoU0r/iytS+dfgPcN39+6qr4NkOTq4bW3rqo3b4+iJUnSzm9zi7ldSOu2+WSSO9K6bx5DCy6PBZ4xHHre1MvOp7W6PAx40XwXLEmSFoctmd2zGri4qr4PvAl4U5JfA14HXDY5qKquS/JBbrxEviRJ0haZc0ipqlNm2fZd4JGzbP+dbaxLkiQtcrts6QuGRd0kSZK2qy0OKcC/Jflakscl2ZrXS5IkbdbWhIx7Aj8A3gn8IMlzhjshS5IkzZstDilV9bWqegJwMPB+4ETgx0lOTnLL+S5QkiQtTlvdXVNV51fV84FfBV4OPBH4YZK3JjlsvgqUJEmL0zaPKamqy6rq9cDtgf9NuxHhN5J8LMmDtvX8kiRpcdrmkJJk1yR/AJwFHA18AnjacO5PJzkrye9t63UkSdListUhJcmyJH8CnAv8PS2kHFFVj6iqt1fVw4AjgK8Dp83hfLsncYVaSZIEbNmKswAkOQB4DnAcsBstoLyhqn4889iq+ibwlCQvnMOpXwIctKX1SJKkndMWhxTafXouBV4L/K+qunRzL6iqn29qf5IjgONpNyyUJEnaqpDyXOAfqura+SggyRLgrcCpwH7zcU5JkrTj25p1Ut4yXwFlcDxtXMv75/GckiRpB7c1LSk3kuSpwBOAR1fVlTP2rQQeBnwX+GJVXTdj/x2BPwbuBhyyiWscCxwLsGLFim0tWdomdcK+8NKbjF3GdlMn7Dt2CTuNJAt+zapa8GvurPy3Pr5s63/QSf4cOAlYOksIuQXw38A64FZVdcHUvl2ALwB/V1X/mOQBwB9W1dGbut6qVatq9erV21TzfEmyU/9C2Nnf39ba2X8uO/v72xH4GfRhZ/8cenp/Sc6oqlUzt8/HDQKvBqqqrkty1yTvSHJT2safAdcAs/3vxFOAw4CTkvwIeA/w6CQfmIeaJEnSDm6z3T1JVgFvAK6a2rwrsA/wSFoImYxR2Rv4fVoXzsQvgf2BG8W1qjqNqfVT5tqSIkmSFoe5jEkp4L60qccrgJ/Sum9+Bbh+6gvgiuHx6qnXT8apXLOtxUqSpMVjLiHlGlp3zkFJ1gEPogWP89gwpExaVK6fev1k23RLzAaq6nPA5+ZUtSRJ2unNZUzK2lm21dS+tbSWFSaPVbVu6tg9gWtnDqqVJEnalLmElHWb2FdTXzB7oFkGXLiFdUmSpEVuW9dJmYST5UnOH86X4fuJ5cC3t/E6kiRpkdnWkDKZWrwO+MHw/X9O7b8JbYDtF7fxOpIkaZGZr5ByaVU9cIOdyUuBOzM11ViSJGku5jImZVPH7EoLKhss1pbkMNp9ef6xqs7duvIkSdJiNZeQsgQgya8Nz1cCtxu+33X42mU45hVJVia5F/AZ4HvceGE3SZKkOZlLd89SWkvJOcPzj83YtwRYkmQZ8EzgubTgcjpwzMybDkqSJM3FXELKJcCrGBZ1G7aFNrX4OmC34TzXAE8Efpt2V+RHAW9K8nK7eyRJ0pbabEipqu8BL9rY/iS7A7tX1VpaK8vHkvwZrZvnxcATk7wCeGVVXbux80iSJE2bj7sgL6OtjbL7ZENVXVZVrwBWAT8GngXcZh6uJUmSFoltnYIMbazKO5gl8FTVOUkeAhw2tMhI0g5nv/32Y82aNQt6zWSDSZPb1fLly7n44osX9JrS5mxzSKmqfwH+Zeb2JLtW1dqqOh84f8NXStKOYc2aNVTV5g/cgS10KJLmYqu6e5L8SpJDk+y6icNOTfKdJC9OctOtK0+SJC1Wcw4pSXZJcrPh6cnAN4H9ktx5lmMPAQ6hranyMuDz216qJElaTOYUUpLsS5u5c3qSJcAvh11XAO9M8tEkKybHV9U5VXUf4BG06cqbupOyJEnSBjYbUpIcBJwJPJS2Zsr+tJBSwO1p66M8HPiPJM+bet0etCnIBbxkvguXJEk7t7m0pPw3cDXw/Kr6rar6GXA9cG1VnV1V9wDuSrvT8euSfCTJ7WldPA8EXltVH95O9UuSpJ3UXBZzuybJfarql1Ob1zLVhVNVZwMPHxZtewGtZWUd8NKqOnGea5YkSYvAZkNKkq8Cn0qyBriW1n3z67T79ZxI6/65Na3r52BaOAnweQOKJEnaWpsMKUmWA/cYvmbzYtrg2R8AXwNeD3wIeCpwUpJjq+rU+StXkiQtFptrSdkN+CPagNnLgMtpoeQpwNOAW1TVFbO87uQkvwm8Nsknquq8+StZkiQtBpscOFtVF1TVacD3gHVV9eVh/MmFbXcLKEmen+ToGS//E2Bv4O/nv2xJkrSzm8sU5CcAXwUO3cRhhwOvm95QVauBL+BCbpIkaSvMZQryfwJ7AL+yiWM+COyf5E4ztj+7ql6+lbVJkqRFbC5TkM9K8nXgSUkmA2hvA+yZ5LPD86W0GT3vSXLB9MuTfBd4TlVdN491S5Kkndxc793zQeBA2kDa3WjhJlPP1wJraPfqmWxbSpuqfCzwP+exZkmStAhstiVl8MXh8alV9b0kJwN/VlX3mxyQ5G+BJwMPnxpQexRwOnBv4J/nrepO1An7wktvMnYZ202dsO/YJUiSFrG5hpSzaC0nvwX8NW2MSpLsNtWNcyat1eSRwHuGbZcC/w58ab4K7kle9kuqauwytpsk1EvHrkKStFjNqbunqtbQFmybBJKbzHiENsA2wJOmtp1RVfepqvdua6GSJGlxmWtLCsARVXXl8P27aSvMXjW1/zzgo7TuHQCmjpckSdoicw4p04Gjqj4DfGbG/vOAo+avNEmStJjNdXaPJEnSgjKkSJKkLhlSJElSlwwpkiSpS4YUSZLUJUOKJEnqkiFFkiR1yZAiSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLW3IXZEmDJGOXsN0sX7587BIkCTCkSFusqhb0ekkW/JqS1AO7eyRJUpcMKZIkqUuGFEmS1CVDiiRJ6pIhRZIkdcmQIkmSumRIkSRJXTKkSJKkLhlSJElSlwwpkiSpS6OHlCRHJvlqknOTfCvJ48euSZIkjW/Ue/ckWQa8BXhCVZ2d5NbAV5N8rarOHbM2SZI0rlFDSlVdCRwytWkFsBa4apyKJElSL7q5C3KS84FfBY6vqp+OXY8kSRpXNyEFWAncFTgtybeq6uOTHUmOBY4FWLFixTjVSVq06oR94aU3GbuM7apO2HfsEqQNpKrGruFGkvwG8Nyq+p+z7V+1alWtXr16gauaXRJ6+/nNp539/e0o/BzGtxg+g8XwHrfUzv4z6en9JTmjqlbN3D7q7J4kD0/yzJmbgSvGqEeSJPVj7CnIZwHPT3IngCS3AE4G3jpqVZIkaXRjz+75eZIn08ah3By4FHhZVf3bmHVJkqTxjT5wtqq+CNxz7DokSVJfxu7ukSRJmpUhRZIkdcmQIkmSumRIkSRJXTKkSJKkLhlSJElSlwwpkiSpS4YUSZLUJUOKJEnqkiFFkiR1yZAiSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLhhRJktQlQ4okSeqSIUWSJHXJkCJJkrpkSJEkSV0ypEiSpC4ZUiRJUpcMKZIkqUuGFEmS1CVDiiRJ6pIhRZIkdcmQIkmSumRIkSRJXTKkSJKkLhlSJElSlwwpkiSpS4YUSZLUJUOKJEnqkiFFkiR1yZAiSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLhhRJktQlQ4okSeqSIUWSJHXJkCJJkrpkSJEkSV1aMnYBO7okY5ew3SxfvnzsEqRu7Mz/1sF/7xuzM3/uO8JnbkjZBlW1oNdLsuDXlOS/9cXKz318dvdIkqQuGVIkSVKXDCmSJKlLhhRJktQlQ4okSeqSIUWSJHXJkCJJkrpkSJEkSV0ypEiSpC4ZUiRJUpdGDylJbpfkI0l+mOS7SZ45dk2SJGl8o4aUJHsApwNvrqqDgCOBo5M8asy6JEnS+MZuSbkb8N2q+ihAVV0AvAp4+KhVSZKk0Y16F+Sq+grwmBmbDwF+MEI5kiSpI6OGlJmS3B14PHD/GduPBY4FWLFixQiVSdsuyYK/1tu+j8vPfHHyc58/3YSUJHcC3gY8pqoun95XVacCpwKsWrVq5/wktNPbWX+JaOP8zBcnP/f500VISXIY8G7gcVVlV48kSRp94CxJDgXeAzyhqs4Zux5JktSHUVtSktwR+CywFPjIVF/cWVU1c0CtJElaRMae3fMd4BZj1iBJkvo0enePJEnSbAwpkiSpS4YUSZLUJUOKJEnqkiFFkiR1yZAiSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLhhRJktQlQ4okSeqSIUWSJHXJkCJJkrpkSJEkSV0ypEiSpC4ZUiRJUpcMKZIkqUuGFEmS1CVDiiRJ6pIhRZIkdcmQIkmSumRIkSRJXTKkSJKkLhlSJElSlwwpkiSpS4YUSZLUJUOKJEnqkiFFkiR1yZAiSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLhhRJktQlQ4okSeqSIUWSJHXJkCJJkrpkSJEkSV0ypEiSpC4ZUiRJUpcMKZIkqUuGFEmS1CVDiiRJ6pIhRZIkdcmQIkmSumRIkSRJXTKkSJKkLhlSJElSlwwpkiSpS4YUSZLUJUOKJEnqkiFFkiR1yZAiSZK6ZEiRJEldMqRIkqQujR5S0nw4yeVJHjJ2PZIkqQ+jh5RqjgLeN3YtkiSpH6OHFEmSpNkYUiRJUpeWjF3AXCQ5FjgWYMWKFSNXs+2SLPhrq2qrrylJ0hh2iJaUqjq1qlZV1aoDDjhg7HK2WVUt+JckSTuaHSKkSJKkxceQIkmSumRIkSRJXepm4GxVPXnsGiRJUj9sSZEkSV0ypEiSpC4ZUiRJUpcMKZIkqUuGFEmS1CVDiiRJ6pIhRZIkdcmQIkmSumRIkSRJXTKkSJKkLhlSJElSlwwpkiSpS4YUSZLUJUOKJEnqUqpq7Bq2SJILgfPGrmMk+wMXjV2EFpyf++LjZ744LebP/TZVdcDMjTtcSFnMkqyuqlVj16GF5ee++PiZL05+7huyu0eSJHXJkCJJkrpkSNmxnDp2ARqFn/vi42e+OPm5z+CYFEmS1CVbUiRJUpcMKZIkdSLJ7kleNHYdvTCk7ADSfDjJ5UkeMnY9WhhJbpfkI0l+mOS7SZ45dk3avpIcmeSrSc5N8q0kjx+7Ji24lwCHjV1ELwwpO4BqjgLeN3YtWhhJ9gBOB95cVQcBRwJHJ3nUuJVpe0myDHgLcGxV3RZ4OPD6JLcdtzItlCRHAMePXUdPDClSn+4GfLeqPgpQVRcAr6L94dJOqKqurKpDqursYdMKYC1w1YhlaYEkWQK8FWf43IghRepQVX2lqh4zY/MhwA/GqEcLK8n5wJeA/7+qfjp2PVoQxwPnAu8fu5CeLBm7AEmbl+TuwOOB+49dixbESuCuwGlJvlVVHx+5Hm1HSe4I/DGtBfWQkcvpii0pUueS3Al4G/C7VXX52PVo+6uqdVV1BvBc4Nix69H2k2QX4DTghVX187Hr6Y0hRepYksOAdwOPryq7enZiSR4+ywyuAFeMUY8WzFNos3lOSvIj4D3Ao5N8YNSqOmFIkTqV5FDaL6wnVNU5Y9ej7e4s4PlDyxlJbgGcTBtMqZ1UVZ1WVTetqpVVtRJ4AvDBWcakLUqOSZE6NPRRfxZYCnwkyWTXWf7y2jlV1c+TPJk2DuXmwKXAy6rq38atTBqP9+6RJEldsrtHkiR1yZAiSZK6ZEiRJEldMqRIkqQuGVIkSVKXDCmSJKlLhhRJWyXJgUn2G7uOzUlycJLfS7LRe6IkeU6S5yXZeyFrk7RprpMiac6S3A24Q1W9O8kfAm8BVlfVPZIsBW5VVedu4vW/BtwUuBq4Hlg345BdgF2BPWi/n742DzU/Hfg74F1VdfRGjvkGsEdV3WFbrydp/rjirKSNGlpK7gocCTwauAtwbZJzgEuGwz6d5CbAl4BlSe5WVZdscLLmqcAL5nj5LwP33arCb+xOw+NJmzjmKuDiebiWpHlkSJG0gSR7AWcDtxs2FXAB8FrgJ8DNgcuGfRdX1aVJ3gq8DnhvkkdU1fWznPqa4fGQqvrOJq7/I1pw2Jb38GJgBfAw4HLguCS7AXsDr66qb00dfu22XEvS9mFIkbSBqroiyRuB3YEP024lf31VHT85JsmklePa4TWvT/JQYPnwdeFspx4ez5m6H9HGfH/r3wEAhwJ3pwWVc2mtQbsBy4AHJTmCFoSuo3VBkeQoWnfTnsCPqurft7EGSdvAkCJpVlX1N5PvNxIorpgcOrXtacBFVbW5lonfBH64if2fmW1jkl+ndQM9b0Z9y2gtP+cBD63m94bQ9EngYVX1/anjjwfeOcslPjT1/QmAIUUakSFF0gaSPAR4Iu1OvFfTWiPWDl0oE/sPjw9LspzWArEE2GcYo/IXVfXjmaceHq+kdcFszLrhfDdSVf83yauBVyb5eFV9b9h1MnAg8OC68WyAhwFnAbdP8grglKr69NS170YLSx8enh8F7Af8gPVdU5JGYkiRNJuDaYNcYX1LSQEvnDpmEjgeCjyQNjNn96ntrwZmhpRlw+Pn5lDDBRvZ/lLgkcDbktwfuA/wbODpVXX+DcW15p/fAU6hjUN5LOsH7U7Gu1xWVZckuR5g+H7ye3HtHGqUtB25Toqk2byL1jKxtKp2oXWxfKGq9p58DfsBXlNVy6pqKW0MyD7ALYD/nOW8rwV+FTiA1mJxE2Df4TX7Ds/3ow3MPWq2woaupGOAewIvBt4GfLKq/n7GoQ8GVgLvY32ryMwpz5I6ZkuKpA1U1WWsn72zsWMuT3IFcLOpzacCS4EnVdXPAZI8FjiE1sVzLW2g6vW0loqZCzWF9d1GS5LsMZzvZ1V12tS1z0xyMvAy2lToB9zoJK0V5XW0cTPPA24/7Hp7kj1pA4GhTZnee7gmw/d7bep9S1o4hhRJc7U0yWNo66V8q6peQxu7cWuAYbbMKtpA1W9Mve4oWstH0ULK9axfyK1oLSi70MJGhu+XDF+T7qPPsD5YTHx+eDwf+Pn0jqqqJJcDP6O17iwddl1Jm0I9CUdnzzjnJoOZpIVlSJG0gSQrgdsAd6At5nYobVrx+4GLgM8Oh34D+PXh+1fRxno8o6qmu1WOG7ZdPaxTskdV3TBoNsnngIOr6tYbqWUP2tTh6W3LaK02/wXcGfhz4MTpY6rqvlPHH0WbuXNcVf0oyZOHXc+kBZmXD89fTOt6esfsPxlJC8mQImk2b6ZNE4bW+rEWOIc24+cbUzNovgD8XpJX0mbSHF9VN5paXFVXTj19CPCeJH9XVdMrzy5JckfWL4u/O63b5fKqOpMNZ9q8itaCcw/gGcCLk3yoqs7awvf56ar6fpI/GWr9YJL9N/MaSQvEgbOSZvMh4OPA0bSpxmcCF1TV2TOm+E7WFXkB8Ang9Zs572/T7stzyoztB9JC0LdprTOrad05/9/MEyR5APAs4M+r6pvAn9K6nf4hye4zjl06hJ+7DpvemOQ/aANzJXXOlhRJszmlqv5u8mS2xdyGwalPYf2U4+dOunmSHFpV/zHj+H1pLTG70mb/TN+I8Ce0WT/TY1KWMuN/pIaBrW+lTWF+A0BVXZXkaOArtIG0LxqOXU0bjzJtJW2Btks29wOQND5bUiRtYEZrCcxYWC3JKlpXz1/RAsM64B3DzBmA05K8bRiDMvEs2nolpwMfSHL4zGtW1bqqur6qrq6qS6pq5k3/Xk0bG3PMdI1VdQZt/ZTjk9xr2Hw+LZCcyPrxKr9bVU9l/b16HjUEnAOBA4fvH7upn42khWNLiqRZDTcZvDtt9s3hwNeGGTx/A9yPNr33uKr62yQn0ELCR5K8nLaS682q6rrhXIcAL6Gtv/Is2h2Tv5rkNbS1UTZWQ2itLkur6odVdRxtIO4Gqupk2sqzE4+datl50rBtspjcJDy9dsZp/nHq+w1WvJW0sAwpkmY13GTwVcCkZeJ7VXV2kk/RphD/YVVNumxOpLWS/Clt9VlogYQktwH+lfZH/6RhfZUjgTfRgkuASvIL2hThq2gDZ5fRAtIetGDyt1tY//QMo72Hx8kaKJMpybefvqfPUO/+tJsj3mh8i6SFZ0iRtCl/DdyXNt7j/QBVdRJw0vRBQ9fL8Um+MOzbldbiAnAYrYvmlZNAMCwWd0ySk2g3JTwSOIg2oHW2cPDdbXwfk5AyaUnZbxPHrgN+wfqxNpJGkg27niVpfiW5A3BeVV09h2P3APaktaBMgsKaqtrqG/4l2YfWrXTRXGqQ1AdDiiRJ6pKzeyRJUpcMKZIkqUuGFEmS1CVDiiRJ6pIhRZIkden/Ade6qGhuK5TRAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#模拟一组数据\n",
    "y1 = [1,2,3,4,5,6]\n",
    "y2 = [2,3,4,4,5,7]\n",
    "y3 = [4,4,3,2,2,3]\n",
    "y4 = [5,6,6,3,2,2]\n",
    "data = pd.DataFrame({\"A\":y1, \"B\":y2, \"C\":y3, \"D\":y4})\n",
    "fig1, ax1 = plt.subplots(figsize=(9,8))\n",
    "plt.xlabel(\"我是x轴\", fontsize = 20)\n",
    "plt.ylabel(\"我是y轴\", fontsize = 20)\n",
    "plt.title('我是标题', fontsize = 20)\n",
    "plt.tick_params(labelsize=13)\n",
    "ax1.boxplot(data)\n",
    "plt.savefig(\"O_01.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上图为最基本的Matplotlib箱线图图绘制，有关boxplot其他参数说明如下:\n",
    ">matplotlib.pyplot.boxplot(x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None, showfliers=None, boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_xticks=True, autorange=False, zorder=None, hold=None, data=None)\n",
    "\n",
    "部分参数解释如下所示:\n",
    "\n",
    "- x: 数组 data\n",
    "- notch: 如果为True, 将生成一个带缺口的箱形图。否则,将生成一个矩形箱线图。 缺口表示中位数附近的置信区间（CI）。\n",
    "- sym: 传单 **\n",
    "- vert: 如果为True, 则使箱线图垂直绘制。 如果为False，则所有内容均为水平绘制。\n",
    "- whis: float, 确定晶须到达第一和第三四分位数以外的范围。\n",
    "- bootstrap: int, 指定是否在带槽的箱形图的中间值附近引导置信区间。\n",
    "- usermedians: array-like, 其第一维（或长度）与x兼容的数组或序列。 这会覆盖由matplotlib为usermedians的每个元素计算的中位数，该中位数不是None。当usermedians的元素为None时，matplotlib将正常计算中间值。\n",
    "- conf_intervals: array-like,第一维（或长度）与x兼容且第二维为2的数组或序列。当conf_intervals的a元素不为None时，将覆盖matplotlib计算的槽口位置（如果notch为True)。当conf_intervals的元素为None时，槽口由其他kwargs指定的方法（例如bootstrap）计算。\n",
    "- positions: 设置盒子的位置。 刻度和限制会自动设置为与位置匹配。 默认值为range（1，N + 1），其中N是要绘制的框数。\n",
    "- widths : 用标量或序列设置每个框的宽度。 如果较小，则默认值为0.5或0.15 *（极限位置之间的距离）。\n",
    "- patch_artist : bool, 使用Line2D artist产生盒子的图形, 否则使用Patch artists画盒子。\n",
    "- labels : 每个数据集的标签。 长度必须与x的尺寸对应。\n",
    "- manage_xticks : bool, optional (True)调整刻度\n",
    "- autorange : bool, optional (False),当True和数据分布使得第25个百分位数和第75个百分位数相等时, whis设置为“范围”, 以使晶须末端位于数据的最小值和最大值。\n",
    "- meanline : bool, optional(False), 如果为True(且showmeans为True),则将根据meanprops尝试将均值渲染为跨越框的整个宽度的线。如果showpotches也为True, 则不建议使用。否则，均值将显示为点。\n",
    "- zorder : 设置箱线图的zorder。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础箱线图 - 改变方向、改变颜色、增加标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y1 = [1,2,3,4,5,6]\n",
    "y2 = [2,3,4,4,5,7]\n",
    "y3 = [4,4,3,2,2,3]\n",
    "y4 = [5,6,6,3,2,2]\n",
    "data = pd.DataFrame({\"A\":y1, \"B\":y2, \"C\":y3, \"D\":y4})\n",
    "\n",
    "#green_diamond = dict(markerfacecolor='g', marker='D')\n",
    "fig3, ax3 = plt.subplots(figsize=(9,8))\n",
    "ax3.set_title('我是标题', fontsize = 20)\n",
    "plt.xlabel(\"我是x轴\", fontsize = 20)\n",
    "plt.ylabel(\"我是y轴\", fontsize = 20)\n",
    "plt.tick_params(labelsize=13)\n",
    "ax3.boxplot(data,labels = labels ,flierprops=green_diamond, vert=False, patch_artist = True,boxprops = {'color':'black','facecolor':'yellow'})\n",
    "plt.savefig(\"O_02.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基础箱线图 - 显示异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig3, ax3 = plt.subplots(figsize=(9,8))\n",
    "ax3.set_title('我是标题', fontsize = 20)\n",
    "plt.xlabel(\"我是x轴\", fontsize = 20)\n",
    "plt.ylabel(\"我是y轴\", fontsize = 20)\n",
    "plt.tick_params(labelsize=13)\n",
    "blue_diamond = dict(markerfacecolor='b', marker='D')\n",
    "np.random.seed(100)\n",
    "data=np.random.normal(size=(1500,4), loc=0, scale=1) #表示有4组数据，每组数据有100个\n",
    "labels=['A','B','C','D']\n",
    "plt.boxplot(data,labels=labels,flierprops=blue_diamond,patch_artist = True,boxprops = {'color':'black','facecolor':'grey'})\n",
    "plt.savefig(\"O_03.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
